An improved quantum principal component analysis algorithm based on the quantum singular threshold method

Publication date: Available online 20 June 2019Source: Physics Letters AAuthor(s): Jie Lin, Wan-Su Bao, Shuo Zhang, Tan Li, Xiang WangAbstractQuantum principal component analysis (qPCA) is a dimensionality reduction algorithm for getting the eigenvectors corresponding to top several eigenvalues of the data matrix and then reconstructing. However, qPCA can only construct the quantum state contains all the eigenvectors and eigenvalues. In this paper, we present an improved quantum principal component analysis (Improved qPCA) algorithm with a fixed threshold. We can reduce the singular value less than the threshold to 0 and obtain a target quantum state which can be used to get an output similar to qPCA after phase estimation. Compared with qPCA, our algorithm has only the target eigenvalues and the probability that we get each target eigenvalue is greater. Furthermore, our algorithm can serve as an additional regularization method and a subroutine for subsequent data processing.

Source: Physics Letters A - June 20, 2019 Category: Physics Source Type: research